# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# from huggingface_hub import PyTorchModelHubMixin


# class BaseTrainer(PyTorchModelHubMixin):
#     r"""
#     Base class for all trainers - this base class implements the basic functions that we
#     need for a trainer.

#     The trainer needs to have the following functions:
#         - step: takes in a batch of data and performs a step of training
#         - loss: takes in a batch of data and returns the loss
#         - compute_rewards: takes in a batch of data and returns the rewards
#         - _build_models_and_tokenizer: builds the models and tokenizer
#         - _build_dataset: builds the dataset
#     Each user is expected to implement their own trainer class that inherits from this base
#     if they want to use a new training algorithm.
#     """

#     def __init__(self, config):
#         self.config = config

#     def step(self, *args):
#         raise NotImplementedError("Not implemented")

#     def loss(self, *args):
#         raise NotImplementedError("Not implemented")

#     def compute_rewards(self, *args):
#         raise NotImplementedError("Not implemented")

#     def _save_pretrained(self, save_directory):
#         raise NotImplementedError("Not implemented")
